Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	417	435
Missing cells (%)	7.8%	8.1%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 74 (16.6%) missing values	`Age` has 90 (20.2%) missing values	Missing
`Cabin` has 343 (76.9%) missing values	`Cabin` has 345 (77.4%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 302 (67.7%) zeros	`SibSp` has 305 (68.4%) zeros	Zeros
`Parch` has 346 (77.6%) zeros	`Parch` has 340 (76.2%) zeros	Zeros
`Fare` has 9 (2.0%) zeros	`Fare` has 5 (1.1%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2024-02-26 14:53:39.358022	2024-02-26 14:53:43.744644
Analysis finished	2024-02-26 14:53:43.743559	2024-02-26 14:53:47.704923
Duration	4.39 seconds	3.96 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	447.57623	445.80493

	Dataset A	Dataset B
Minimum	1	1
Maximum	891	889
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	1
5-th percentile	48.5	33.25
Q1	232.5	222.5
median	456	457.5
Q3	664.75	665.5
95-th percentile	844.25	850
Maximum	891	889
Range	890	888
Interquartile range (IQR)	432.25	443

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	257.3442	260.46221
Coefficient of variation (CV)	0.57497289	0.58425152
Kurtosis	-1.1901699	-1.1936929
Mean	447.57623	445.80493
Median Absolute Deviation (MAD)	215.5	222.5
Skewness	-0.005753219	-0.036756423
Sum	199619	198829
Variance	66226.038	67840.562
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
339	1	0.2%
42	1	0.2%
399	1	0.2%
112	1	0.2%
743	1	0.2%
209	1	0.2%
43	1	0.2%
275	1	0.2%
834	1	0.2%
595	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
46	1	0.2%
761	1	0.2%
42	1	0.2%
820	1	0.2%
28	1	0.2%
307	1	0.2%
256	1	0.2%
364	1	0.2%
202	1	0.2%
314	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
6	1	0.2%
8	1	0.2%
10	1	0.2%
11	1	0.2%
14	1	0.2%
17	1	0.2%
21	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
10	1	0.2%
13	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
10	1	0.2%
13	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
6	1	0.2%
8	1	0.2%
10	1	0.2%
11	1	0.2%
14	1	0.2%
17	1	0.2%
21	1	0.2%
22	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	267
1	179

0	277
1	169

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	1	0
2nd row	0	1
3rd row	1	0
4th row	0	0
5th row	1	1

Common Values

Value	Count	Frequency (%)
0	267	59.9%
1	179	40.1%

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	267	59.9%
1	179	40.1%

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Most occurring characters

Value	Count	Frequency (%)
0	267	59.9%
1	179	40.1%

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	267	59.9%
1	179	40.1%

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	267	59.9%
1	179	40.1%

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	267	59.9%
1	179	40.1%

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	236
1	105
2	105

3	252
1	104
2	90

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	3
2nd row	1	2
3rd row	2	3
4th row	1	3
5th row	2	2

Common Values

Value	Count	Frequency (%)
3	236	52.9%
1	105	23.5%
2	105	23.5%

Value	Count	Frequency (%)
3	252	56.5%
1	104	23.3%
2	90	20.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	236	52.9%
1	105	23.5%
2	105	23.5%

Value	Count	Frequency (%)
3	252	56.5%
1	104	23.3%
2	90	20.2%

Most occurring characters

Value	Count	Frequency (%)
3	236	52.9%
1	105	23.5%
2	105	23.5%

Value	Count	Frequency (%)
3	252	56.5%
1	104	23.3%
2	90	20.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	236	52.9%
1	105	23.5%
2	105	23.5%

Value	Count	Frequency (%)
3	252	56.5%
1	104	23.3%
2	90	20.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	236	52.9%
1	105	23.5%
2	105	23.5%

Value	Count	Frequency (%)
3	252	56.5%
1	104	23.3%
2	90	20.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	236	52.9%
1	105	23.5%
2	105	23.5%

Value	Count	Frequency (%)
3	252	56.5%
1	104	23.3%
2	90	20.2%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	67	67
Median length	49	50
Mean length	26.730942	27.107623
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	11922	12090
Distinct characters	59	60
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Dahl, Mr. Karl Edwart	Rogers, Mr. William John
2nd row	Chaffee, Mr. Herbert Fuller	Mellors, Mr. William John
3rd row	Cameron, Miss. Clear Annie	Goodwin, Mrs. Frederick (Augusta Tyler)
4th row	Giglio, Mr. Victor	Olsen, Mr. Karl Siegwart Andreas
5th row	Richards, Master. George Sibley	Hewlett, Mrs. (Mary D Kingcome)

Value	Count	Frequency (%)
mr	254	14.0%
miss	95	5.3%
mrs	66	3.7%
william	30	1.7%
john	25	1.4%
henry	20	1.1%
master	18	1.0%
james	14	0.8%
mary	12	0.7%
george	11	0.6%
Other values (878)	1263	69.9%

Value	Count	Frequency (%)
mr	265	14.5%
miss	88	4.8%
mrs	66	3.6%
william	30	1.6%
master	20	1.1%
john	19	1.0%
henry	17	0.9%
thomas	17	0.9%
james	15	0.8%
edward	13	0.7%
Other values (890)	1281	70.0%

Most occurring characters

Value	Count	Frequency (%)
	1362	11.4%
r	973	8.2%
e	845	7.1%
a	790	6.6%
i	666	5.6%
n	638	5.4%
s	638	5.4%
M	560	4.7%
l	541	4.5%
o	509	4.3%
Other values (49)	4400	36.9%

Value	Count	Frequency (%)
	1387	11.5%
r	967	8.0%
e	868	7.2%
a	846	7.0%
n	652	5.4%
s	643	5.3%
i	635	5.3%
M	573	4.7%
o	514	4.3%
l	513	4.2%
Other values (50)	4492	37.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7660	64.3%
Uppercase Letter	1816	15.2%
Space Separator	1362	11.4%
Other Punctuation	937	7.9%
Open Punctuation	70	0.6%
Close Punctuation	70	0.6%
Dash Punctuation	7	0.1%

Value	Count	Frequency (%)
Lowercase Letter	7754	64.1%
Uppercase Letter	1846	15.3%
Space Separator	1387	11.5%
Other Punctuation	947	7.8%
Open Punctuation	75	0.6%
Close Punctuation	75	0.6%
Dash Punctuation	6	< 0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1362	100.0%

Value	Count	Frequency (%)
	1387	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	973	12.7%
e	845	11.0%
a	790	10.3%
i	666	8.7%
n	638	8.3%
s	638	8.3%
l	541	7.1%
o	509	6.6%
t	324	4.2%
h	254	3.3%
Other values (16)	1482	19.3%

Value	Count	Frequency (%)
r	967	12.5%
e	868	11.2%
a	846	10.9%
n	652	8.4%
s	643	8.3%
i	635	8.2%
o	514	6.6%
l	513	6.6%
t	341	4.4%
h	267	3.4%
Other values (16)	1508	19.4%

Uppercase Letter

Value	Count	Frequency (%)
M	560	30.8%
A	119	6.6%
J	113	6.2%
H	103	5.7%
S	88	4.8%
E	86	4.7%
B	78	4.3%
C	75	4.1%
W	69	3.8%
L	66	3.6%
Other values (15)	459	25.3%

Value	Count	Frequency (%)
M	573	31.0%
A	125	6.8%
J	111	6.0%
H	101	5.5%
S	89	4.8%
C	85	4.6%
E	83	4.5%
B	73	4.0%
W	69	3.7%
L	68	3.7%
Other values (15)	469	25.4%

Other Punctuation

Value	Count	Frequency (%)
.	447	47.7%
,	446	47.6%
"	42	4.5%
'	2	0.2%

Value	Count	Frequency (%)
.	447	47.2%
,	446	47.1%
"	48	5.1%
'	5	0.5%
/	1	0.1%

Open Punctuation

Value	Count	Frequency (%)
(	70	100.0%

Value	Count	Frequency (%)
(	75	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	70	100.0%

Value	Count	Frequency (%)
)	75	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	7	100.0%

Value	Count	Frequency (%)
-	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9476	79.5%
Common	2446	20.5%

Value	Count	Frequency (%)
Latin	9600	79.4%
Common	2490	20.6%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1362	55.7%
.	447	18.3%
,	446	18.2%
(	70	2.9%
)	70	2.9%
"	42	1.7%
-	7	0.3%
'	2	0.1%

Value	Count	Frequency (%)
	1387	55.7%
.	447	18.0%
,	446	17.9%
(	75	3.0%
)	75	3.0%
"	48	1.9%
-	6	0.2%
'	5	0.2%
/	1	< 0.1%

Latin

Value	Count	Frequency (%)
r	973	10.3%
e	845	8.9%
a	790	8.3%
i	666	7.0%
n	638	6.7%
s	638	6.7%
M	560	5.9%
l	541	5.7%
o	509	5.4%
t	324	3.4%
Other values (41)	2992	31.6%

Value	Count	Frequency (%)
r	967	10.1%
e	868	9.0%
a	846	8.8%
n	652	6.8%
s	643	6.7%
i	635	6.6%
M	573	6.0%
o	514	5.4%
l	513	5.3%
t	341	3.6%
Other values (41)	3048	31.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11922	100.0%

Value	Count	Frequency (%)
ASCII	12090	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1362	11.4%
r	973	8.2%
e	845	7.1%
a	790	6.6%
i	666	5.6%
n	638	5.4%
s	638	5.4%
M	560	4.7%
l	541	4.5%
o	509	4.3%
Other values (49)	4400	36.9%

Value	Count	Frequency (%)
	1387	11.5%
r	967	8.0%
e	868	7.2%
a	846	7.0%
n	652	5.4%
s	643	5.3%
i	635	5.3%
M	573	4.7%
o	514	4.3%
l	513	4.2%
Other values (50)	4492	37.2%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	284
female	162

male	291
female	155

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7264574	4.6950673
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2108	2094
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	male
3rd row	female	female
4th row	male	male
5th row	male	female

Common Values

Value	Count	Frequency (%)
male	284	63.7%
female	162	36.3%

Value	Count	Frequency (%)
male	291	65.2%
female	155	34.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	284	63.7%
female	162	36.3%

Value	Count	Frequency (%)
male	291	65.2%
female	155	34.8%

Most occurring characters

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2108	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2094	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2108	100.0%

Value	Count	Frequency (%)
Latin	2094	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2108	100.0%

Value	Count	Frequency (%)
ASCII	2094	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	79	73
Distinct (%)	21.2%	20.5%
Missing	74	90
Missing (%)	16.6%	20.2%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.730054	30.452725

	Dataset A	Dataset B
Minimum	0.67	0.42
Maximum	74	71
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.67	0.42
5-th percentile	4.55	5
Q1	20.375	21
median	29	29
Q3	39	39
95-th percentile	53.45	56
Maximum	74	71
Range	73.33	70.58
Interquartile range (IQR)	18.625	18

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.105994	14.422212
Coefficient of variation (CV)	0.47446916	0.47359349
Kurtosis	-0.04423976	0.028659718
Mean	29.730054	30.452725
Median Absolute Deviation (MAD)	9	9
Skewness	0.24681164	0.32642933
Sum	11059.58	10841.17
Variance	198.97906	208.00021
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
21	16	3.6%
19	16	3.6%
24	15	3.4%
29	13	2.9%
30	12	2.7%
22	12	2.7%
18	12	2.7%
36	12	2.7%
28	11	2.5%
32	11	2.5%
Other values (69)	242	54.3%
(Missing)	74	16.6%

Value	Count	Frequency (%)
22	16	3.6%
18	14	3.1%
19	14	3.1%
24	13	2.9%
29	11	2.5%
27	11	2.5%
25	11	2.5%
26	10	2.2%
36	10	2.2%
16	10	2.2%
Other values (63)	236	52.9%
(Missing)	90	20.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	1	0.2%
0.83	2	0.4%
1	3	0.7%
2	5	1.1%
3	3	0.7%
4	4	0.9%
5	3	0.7%
6	2	0.4%
7	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
1	3	0.7%
2	3	0.7%
3	2	0.4%
4	6	1.3%
5	4	0.9%
6	2	0.4%
8	3	0.7%
9	4	0.9%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
1	3	0.7%
2	3	0.7%
3	2	0.4%
4	6	1.3%
5	4	0.9%
6	2	0.4%
8	3	0.7%
9	4	0.9%

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	1	0.2%
0.83	2	0.4%
1	3	0.7%
2	5	1.1%
3	3	0.7%
4	4	0.9%
5	3	0.7%
6	2	0.4%
7	1	0.2%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.50224215	0.55829596

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	302	305
Zeros (%)	67.7%	68.4%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.0139452	1.2029779
Coefficient of variation (CV)	2.0188373	2.1547314
Kurtosis	16.870595	16.759713
Mean	0.50224215	0.55829596
Median Absolute Deviation (MAD)	0	0
Skewness	3.5084051	3.6627275
Sum	224	249
Variance	1.0280848	1.4471557
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	302	67.7%
1	109	24.4%
2	15	3.4%
4	9	2.0%
3	6	1.3%
5	3	0.7%
8	2	0.4%

Value	Count	Frequency (%)
0	305	68.4%
1	99	22.2%
2	16	3.6%
4	9	2.0%
3	9	2.0%
8	5	1.1%
5	3	0.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	302	67.7%
1	109	24.4%
2	15	3.4%
3	6	1.3%
4	9	2.0%
5	3	0.7%
8	2	0.4%

Value	Count	Frequency (%)
0	305	68.4%
1	99	22.2%
2	16	3.6%
3	9	2.0%
4	9	2.0%
5	3	0.7%
8	5	1.1%

Value	Count	Frequency (%)
0	305	68.4%
1	99	22.2%
2	16	3.6%
3	9	2.0%
4	9	2.0%
5	3	0.7%
8	5	1.1%

Value	Count	Frequency (%)
0	302	67.7%
1	109	24.4%
2	15	3.4%
3	6	1.3%
4	9	2.0%
5	3	0.7%
8	2	0.4%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	7
Distinct (%)	1.3%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.35201794	0.40134529

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	6
Zeros	346	340
Zeros (%)	77.6%	76.2%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	5	6
Range	5	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.75807738	0.88832527
Coefficient of variation (CV)	2.1535192	2.2133691
Kurtosis	8.6256736	11.399706
Mean	0.35201794	0.40134529
Median Absolute Deviation (MAD)	0	0
Skewness	2.6398298	3.0441951
Sum	157	179
Variance	0.57468131	0.78912178
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	346	77.6%
1	55	12.3%
2	39	8.7%
5	2	0.4%
3	2	0.4%
4	2	0.4%

Value	Count	Frequency (%)
0	340	76.2%
1	59	13.2%
2	36	8.1%
5	5	1.1%
3	3	0.7%
4	2	0.4%
6	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	346	77.6%
1	55	12.3%
2	39	8.7%
3	2	0.4%
4	2	0.4%
5	2	0.4%

Value	Count	Frequency (%)
0	340	76.2%
1	59	13.2%
2	36	8.1%
3	3	0.7%
4	2	0.4%
5	5	1.1%
6	1	0.2%

Value	Count	Frequency (%)
0	340	76.2%
1	59	13.2%
2	36	8.1%
3	3	0.7%
4	2	0.4%
5	5	1.1%
6	1	0.2%

Value	Count	Frequency (%)
0	346	77.6%
1	55	12.3%
2	39	8.7%
3	2	0.4%
4	2	0.4%
5	2	0.4%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	371	375
Distinct (%)	83.2%	84.1%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7847534	6.8565022
Min length	3	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	3026	3058
Distinct characters	35	35
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	312	327 ?
Unique (%)	70.0%	73.3%

Sample

	Dataset A	Dataset B
1st row	7598	S.C./A.4. 23567
2nd row	W.E.P. 5734	SW/PP 751
3rd row	F.C.C. 13528	CA 2144
4th row	PC 17593	4579
5th row	29106	248706

Value	Count	Frequency (%)
pc	33	5.7%
c.a	15	2.6%
a/5	12	2.1%
2	6	1.0%
ston/o	6	1.0%
w./c	5	0.9%
sc/paris	5	0.9%
c	5	0.9%
ca	5	0.9%
soton/o.q	4	0.7%
Other values (392)	479	83.3%

Value	Count	Frequency (%)
pc	29	5.0%
c.a	18	3.1%
ca	10	1.7%
a/5	7	1.2%
w./c	7	1.2%
2	6	1.0%
ston/o	6	1.0%
sc/paris	6	1.0%
347088	5	0.9%
soton/o.q	5	0.9%
Other values (394)	476	82.8%

Most occurring characters

Value	Count	Frequency (%)
3	354	11.7%
1	335	11.1%
2	287	9.5%
7	253	8.4%
4	235	7.8%
6	220	7.3%
5	201	6.6%
0	197	6.5%
9	161	5.3%
8	151	5.0%
Other values (25)	632	20.9%

Value	Count	Frequency (%)
3	376	12.3%
1	330	10.8%
2	287	9.4%
7	249	8.1%
4	229	7.5%
6	210	6.9%
0	203	6.6%
5	199	6.5%
9	171	5.6%
8	136	4.4%
Other values (25)	668	21.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2394	79.1%
Uppercase Letter	324	10.7%
Other Punctuation	163	5.4%
Space Separator	129	4.3%
Lowercase Letter	16	0.5%

Value	Count	Frequency (%)
Decimal Number	2390	78.2%
Uppercase Letter	350	11.4%
Other Punctuation	173	5.7%
Space Separator	129	4.2%
Lowercase Letter	16	0.5%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	354	14.8%
1	335	14.0%
2	287	12.0%
7	253	10.6%
4	235	9.8%
6	220	9.2%
5	201	8.4%
0	197	8.2%
9	161	6.7%
8	151	6.3%

Value	Count	Frequency (%)
3	376	15.7%
1	330	13.8%
2	287	12.0%
7	249	10.4%
4	229	9.6%
6	210	8.8%
0	203	8.5%
5	199	8.3%
9	171	7.2%
8	136	5.7%

Space Separator

Value	Count	Frequency (%)
	129	100.0%

Value	Count	Frequency (%)
	129	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	113	69.3%
/	50	30.7%

Value	Count	Frequency (%)
.	120	69.4%
/	53	30.6%

Uppercase Letter

Value	Count	Frequency (%)
C	83	25.6%
P	55	17.0%
A	45	13.9%
O	44	13.6%
S	33	10.2%
N	17	5.2%
T	15	4.6%
W	7	2.2%
Q	6	1.9%
F	5	1.5%
Other values (6)	14	4.3%

Value	Count	Frequency (%)
C	87	24.9%
O	51	14.6%
A	48	13.7%
P	48	13.7%
S	39	11.1%
N	20	5.7%
T	18	5.1%
W	11	3.1%
Q	8	2.3%
I	5	1.4%
Other values (6)	15	4.3%

Lowercase Letter

Value	Count	Frequency (%)
a	4	25.0%
s	4	25.0%
r	3	18.8%
i	3	18.8%
l	1	6.2%
e	1	6.2%

Value	Count	Frequency (%)
a	4	25.0%
s	4	25.0%
i	3	18.8%
r	3	18.8%
l	1	6.2%
e	1	6.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	2686	88.8%
Latin	340	11.2%

Value	Count	Frequency (%)
Common	2692	88.0%
Latin	366	12.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	354	13.2%
1	335	12.5%
2	287	10.7%
7	253	9.4%
4	235	8.7%
6	220	8.2%
5	201	7.5%
0	197	7.3%
9	161	6.0%
8	151	5.6%
Other values (3)	292	10.9%

Value	Count	Frequency (%)
3	376	14.0%
1	330	12.3%
2	287	10.7%
7	249	9.2%
4	229	8.5%
6	210	7.8%
0	203	7.5%
5	199	7.4%
9	171	6.4%
8	136	5.1%
Other values (3)	302	11.2%

Latin

Value	Count	Frequency (%)
C	83	24.4%
P	55	16.2%
A	45	13.2%
O	44	12.9%
S	33	9.7%
N	17	5.0%
T	15	4.4%
W	7	2.1%
Q	6	1.8%
F	5	1.5%
Other values (12)	30	8.8%

Value	Count	Frequency (%)
C	87	23.8%
O	51	13.9%
A	48	13.1%
P	48	13.1%
S	39	10.7%
N	20	5.5%
T	18	4.9%
W	11	3.0%
Q	8	2.2%
I	5	1.4%
Other values (12)	31	8.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3026	100.0%

Value	Count	Frequency (%)
ASCII	3058	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	354	11.7%
1	335	11.1%
2	287	9.5%
7	253	8.4%
4	235	7.8%
6	220	7.3%
5	201	6.6%
0	197	6.5%
9	161	5.3%
8	151	5.0%
Other values (25)	632	20.9%

Value	Count	Frequency (%)
3	376	12.3%
1	330	10.8%
2	287	9.4%
7	249	8.1%
4	229	7.5%
6	210	6.9%
0	203	6.6%
5	199	6.5%
9	171	5.6%
8	136	4.4%
Other values (25)	668	21.8%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	172	177
Distinct (%)	38.6%	39.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	33.150223	33.447393

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	9	5
Zeros (%)	2.0%	1.1%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.225	7.225
Q1	7.925	8.05
median	13.64585	14.5
Q3	31.359375	31.3875
95-th percentile	130.2375	112.67708
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	23.434375	23.3375

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	49.965571	52.921279
Coefficient of variation (CV)	1.5072469	1.5822244
Kurtosis	24.57699	33.309281
Mean	33.150223	33.447393
Median Absolute Deviation (MAD)	6.39585	7.25
Skewness	4.0853779	4.8818584
Sum	14784.999	14917.537
Variance	2496.5583	2800.6618
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	24	5.4%
8.05	22	4.9%
10.5	18	4.0%
7.75	18	4.0%
7.8958	15	3.4%
26	14	3.1%
7.25	10	2.2%
0	9	2.0%
7.775	8	1.8%
7.925	8	1.8%
Other values (162)	300	67.3%

Value	Count	Frequency (%)
8.05	23	5.2%
10.5	19	4.3%
7.8958	18	4.0%
13	16	3.6%
7.75	15	3.4%
26	14	3.1%
7.25	9	2.0%
26.55	8	1.8%
8.6625	8	1.8%
7.925	8	1.8%
Other values (167)	308	69.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	9	2.0%
4.0125	1	0.2%
5	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.975	2	0.4%
7.05	3	0.7%
7.125	1	0.2%
7.1417	1	0.2%

Value	Count	Frequency (%)
0	5	1.1%
5	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.975	2	0.4%
7.0458	1	0.2%
7.05	4	0.9%
7.125	1	0.2%
7.1417	1	0.2%

Value	Count	Frequency (%)
0	5	1.1%
5	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.975	2	0.4%
7.0458	1	0.2%
7.05	4	0.9%
7.125	1	0.2%
7.1417	1	0.2%

Value	Count	Frequency (%)
0	9	2.0%
4.0125	1	0.2%
5	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.975	2	0.4%
7.05	3	0.7%
7.125	1	0.2%
7.1417	1	0.2%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	82	91
Distinct (%)	79.6%	90.1%
Missing	343	345
Missing (%)	76.9%	77.4%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	15
Median length	3	3
Mean length	3.6990291	3.5643564
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	381	360
Distinct characters	19	19
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	63	81 ?
Unique (%)	61.2%	80.2%

Sample

	Dataset A	Dataset B
1st row	E31	E101
2nd row	B86	C47
3rd row	B51 B53 B55	E77
4th row	E63	D19
5th row	A10	C126

Value	Count	Frequency (%)
d	3	2.4%
b98	3	2.4%
b96	3	2.4%
b66	2	1.6%
e67	2	1.6%
f33	2	1.6%
c27	2	1.6%
c25	2	1.6%
c23	2	1.6%
b58	2	1.6%
Other values (84)	100	81.3%

Value	Count	Frequency (%)
b18	2	1.7%
b51	2	1.7%
c23	2	1.7%
e101	2	1.7%
c123	2	1.7%
d	2	1.7%
b55	2	1.7%
b53	2	1.7%
c125	2	1.7%
b49	2	1.7%
Other values (91)	97	82.9%

Most occurring characters

Value	Count	Frequency (%)
B	38	10.0%
C	34	8.9%
1	34	8.9%
2	33	8.7%
3	29	7.6%
6	28	7.3%
5	25	6.6%
	20	5.2%
0	20	5.2%
D	19	5.0%
Other values (9)	101	26.5%

Value	Count	Frequency (%)
1	38	10.6%
C	34	9.4%
B	33	9.2%
3	32	8.9%
5	28	7.8%
2	26	7.2%
6	20	5.6%
4	19	5.3%
D	19	5.3%
8	18	5.0%
Other values (9)	93	25.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	238	62.5%
Uppercase Letter	123	32.3%
Space Separator	20	5.2%

Value	Count	Frequency (%)
Decimal Number	227	63.1%
Uppercase Letter	117	32.5%
Space Separator	16	4.4%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
B	38	30.9%
C	34	27.6%
D	19	15.4%
E	19	15.4%
F	6	4.9%
G	3	2.4%
A	3	2.4%
T	1	0.8%

Value	Count	Frequency (%)
C	34	29.1%
B	33	28.2%
D	19	16.2%
E	16	13.7%
A	6	5.1%
F	6	5.1%
G	2	1.7%
T	1	0.9%

Decimal Number

Value	Count	Frequency (%)
1	34	14.3%
2	33	13.9%
3	29	12.2%
6	28	11.8%
5	25	10.5%
0	20	8.4%
8	19	8.0%
4	17	7.1%
7	17	7.1%
9	16	6.7%

Value	Count	Frequency (%)
1	38	16.7%
3	32	14.1%
5	28	12.3%
2	26	11.5%
6	20	8.8%
4	19	8.4%
8	18	7.9%
7	17	7.5%
9	17	7.5%
0	12	5.3%

Space Separator

Value	Count	Frequency (%)
	20	100.0%

Value	Count	Frequency (%)
	16	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	258	67.7%
Latin	123	32.3%

Value	Count	Frequency (%)
Common	243	67.5%
Latin	117	32.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
B	38	30.9%
C	34	27.6%
D	19	15.4%
E	19	15.4%
F	6	4.9%
G	3	2.4%
A	3	2.4%
T	1	0.8%

Value	Count	Frequency (%)
C	34	29.1%
B	33	28.2%
D	19	16.2%
E	16	13.7%
A	6	5.1%
F	6	5.1%
G	2	1.7%
T	1	0.9%

Common

Value	Count	Frequency (%)
1	34	13.2%
2	33	12.8%
3	29	11.2%
6	28	10.9%
5	25	9.7%
	20	7.8%
0	20	7.8%
8	19	7.4%
4	17	6.6%
7	17	6.6%

Value	Count	Frequency (%)
1	38	15.6%
3	32	13.2%
5	28	11.5%
2	26	10.7%
6	20	8.2%
4	19	7.8%
8	18	7.4%
7	17	7.0%
9	17	7.0%
	16	6.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	381	100.0%

Value	Count	Frequency (%)
ASCII	360	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
B	38	10.0%
C	34	8.9%
1	34	8.9%
2	33	8.7%
3	29	7.6%
6	28	7.3%
5	25	6.6%
	20	5.2%
0	20	5.2%
D	19	5.0%
Other values (9)	101	26.5%

Value	Count	Frequency (%)
1	38	10.6%
C	34	9.4%
B	33	9.2%
3	32	8.9%
5	28	7.8%
2	26	7.2%
6	20	5.6%
4	19	5.3%
D	19	5.3%
8	18	5.0%
Other values (9)	93	25.8%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

S	333
C	80
Q	33

S	321
C	83
Q	42

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	S	S
4th row	C	S
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	333	74.7%
C	80	17.9%
Q	33	7.4%

Value	Count	Frequency (%)
S	321	72.0%
C	83	18.6%
Q	42	9.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	333	74.7%
c	80	17.9%
q	33	7.4%

Value	Count	Frequency (%)
s	321	72.0%
c	83	18.6%
q	42	9.4%

Most occurring characters

Value	Count	Frequency (%)
S	333	74.7%
C	80	17.9%
Q	33	7.4%

Value	Count	Frequency (%)
S	321	72.0%
C	83	18.6%
Q	42	9.4%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	446	100.0%

Value	Count	Frequency (%)
Uppercase Letter	446	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	333	74.7%
C	80	17.9%
Q	33	7.4%

Value	Count	Frequency (%)
S	321	72.0%
C	83	18.6%
Q	42	9.4%

Most occurring scripts

Value	Count	Frequency (%)
Latin	446	100.0%

Value	Count	Frequency (%)
Latin	446	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	333	74.7%
C	80	17.9%
Q	33	7.4%

Value	Count	Frequency (%)
S	321	72.0%
C	83	18.6%
Q	42	9.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	333	74.7%
C	80	17.9%
Q	33	7.4%

Value	Count	Frequency (%)
S	321	72.0%
C	83	18.6%
Q	42	9.4%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
338	339	1	3	Dahl, Mr. Karl Edwart	male	45.00	0	0	7598	8.050	NaN	S
92	93	0	1	Chaffee, Mr. Herbert Fuller	male	46.00	1	0	W.E.P. 5734	61.175	E31	S
211	212	1	2	Cameron, Miss. Clear Annie	female	35.00	0	0	F.C.C. 13528	21.000	NaN	S
139	140	0	1	Giglio, Mr. Victor	male	24.00	0	0	PC 17593	79.200	B86	C
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.750	NaN	S
276	277	0	3	Lindblom, Miss. Augusta Charlotta	female	45.00	0	0	347073	7.750	NaN	S
439	440	0	2	Kvillner, Mr. Johan Henrik Johannesson	male	31.00	0	0	C.A. 18723	10.500	NaN	S
635	636	1	2	Davis, Miss. Mary	female	28.00	0	0	237668	13.000	NaN	S
614	615	0	3	Brocklebank, Mr. William Alfred	male	35.00	0	0	364512	8.050	NaN	S
346	347	1	2	Smith, Miss. Marion Elsie	female	40.00	0	0	31418	13.000	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
45	46	0	3	Rogers, Mr. William John	male	NaN	0	0	S.C./A.4. 23567	8.0500	NaN	S
226	227	1	2	Mellors, Mr. William John	male	19.0	0	0	SW/PP 751	10.5000	NaN	S
678	679	0	3	Goodwin, Mrs. Frederick (Augusta Tyler)	female	43.0	1	6	CA 2144	46.9000	NaN	S
197	198	0	3	Olsen, Mr. Karl Siegwart Andreas	male	42.0	0	1	4579	8.4042	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
280	281	0	3	Duane, Mr. Frank	male	65.0	0	0	336439	7.7500	NaN	Q
846	847	0	3	Sage, Mr. Douglas Bullen	male	NaN	8	2	CA. 2343	69.5500	NaN	S
638	639	0	3	Panula, Mrs. Juha (Maria Emilia Ojala)	female	41.0	0	5	3101295	39.6875	NaN	S
241	242	1	3	Murphy, Miss. Katherine "Kate"	female	NaN	1	0	367230	15.5000	NaN	Q
87	88	0	3	Slocovski, Mr. Selman Francis	male	NaN	0	0	SOTON/OQ 392086	8.0500	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
333	334	0	3	Vander Planke, Mr. Leo Edmondus	male	16.0	2	0	345764	18.0000	NaN	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
492	493	0	1	Molson, Mr. Harry Markland	male	55.0	0	0	113787	30.5000	C30	S
399	400	1	2	Trout, Mrs. William H (Jessie L)	female	28.0	0	0	240929	12.6500	NaN	S
357	358	0	2	Funk, Miss. Annie Clemmer	female	38.0	0	0	237671	13.0000	NaN	S
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.0	0	0	PC 17582	153.4625	C125	S
648	649	0	3	Willey, Mr. Edward	male	NaN	0	0	S.O./P.P. 751	7.5500	NaN	S
722	723	0	2	Gillespie, Mr. William Henry	male	34.0	0	0	12233	13.0000	NaN	S
248	249	1	1	Beckwith, Mr. Richard Leonard	male	37.0	1	1	11751	52.5542	D35	S
567	568	0	3	Palsson, Mrs. Nils (Alma Cornelia Berglund)	female	29.0	0	4	349909	21.0750	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
299	300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0	0	1	PC 17558	247.5208	B58 B60	C
503	504	0	3	Laitinen, Miss. Kristina Sofia	female	37.0	0	0	4135	9.5875	NaN	S
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.0	0	0	PC 17582	153.4625	C125	S
582	583	0	2	Downton, Mr. William James	male	54.0	0	0	28403	26.0000	NaN	S
765	766	1	1	Hogeboom, Mrs. John C (Anna Andrews)	female	51.0	1	0	13502	77.9583	D11	S
860	861	0	3	Hansen, Mr. Claus Peter	male	41.0	2	0	350026	14.1083	NaN	S
785	786	0	3	Harmer, Mr. Abraham (David Lishin)	male	25.0	0	0	374887	7.2500	NaN	S
422	423	0	3	Zimmerman, Mr. Leo	male	29.0	0	0	315082	7.8750	NaN	S
516	517	1	2	Lemore, Mrs. (Amelia Milley)	female	34.0	0	0	C.A. 34260	10.5000	F33	S
339	340	0	1	Blackwell, Mr. Stephen Weart	male	45.0	0	0	113784	35.5000	T	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Open Punctuation

Close Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters